* By Jorge López Morales
Open banking, or the open financial system, is a trend that is already present and irreversible, which will promote the creation of increasingly personalized and individualized financial products, increasing competitiveness among market players.
For banking institutions to successfully surf the wave of open banking and promote sustainable innovation, it is essential to follow an API-oriented business strategy. For those who are not familiar with the acronym, APIs are the interfaces that allow the easy connection of a platform with other systems.
At the heart of each API is data. Therefore, having quick access to them is the first starting point in any process. However, while every fintech wants their data to be agile, efficient and scalable, most still deal with a swamp of data in their systems, that is, a complex maze of organizational barriers of historical, recent, real-time and streaming data.
In the world of open banking, nothing happens without the API to integrate a service, a function or part of the data. So, how open banking companies will manage their APIs is an essential issue for them to be able to be agile and innovative. The technology behind API management helps companies create, produce, protect and analyze the set of programming routines and standards as products.
Another innovation that is here to stay is the use of analysis technologies to continuously understand information. Self-service business intelligence tools help humans more easily understand their data. However, these tools analyze only what has already happened. Analyzing historical data generates patterns and the mechanisms observed in the past will continue into the future.
Governance and metadata management, on the other hand, are part of a culture that cannot be imposed on forceps by regulations, provided only by technology or outsourced. But technology can facilitate a culture of continuous data curation. Open Banking companies will have access to account information, sensitive personal data, consented interactions and other data, which makes metadata management a crucial aspect in an architectural project, considering how all the elements (APIs, BI, science of data and data virtualization) can interact with this metadata.
And finally, there is the science of streaming data. Traditional machine learning trains models based on data and this approach considers that the world remains essentially the same, that is, that the same patterns, anomalies and mechanisms observed in the past will continue in the future.
A well-known case of success is that of AA Ireland, one of the largest insurers in Europe, with solutions for cars, travel and homes, which uses streaming data science to generate dynamic pricing and risk assessment models. Instead of using static predictive models, AA Ireland uses current market conditions, increasing or decreasing discounts, for example. This technology helps to explain opportunities and risks existing in the market at that time, not depending on forecasts based on past scenarios.
* Jorge López Morales is vice president of sales for TIBCO Software in Latin America.
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
EN 
PT 
